Tutorials

Chromatographic Column Characterization

Stationary-phase characterization is traditionally performed with a small set of probe compounds selected to address specific retention mechanisms. With the limited number of chemistries exhibited by the probe compounds, however, no single characterization method will be able to address every situation completely. Using a more complex sample with a greater number of probe compounds has generally not been feasible due to the onerous task of managing extremely large amounts of data.

For many years and with varying degrees of success, automated systems for method development have been available and have been used to resolve the often conflicting goals of achieving robustness, good resolution, and quick run times. Modern approaches use a combined strategy of method screening and optimization of variables such as gradient and temperature. Previously, upfront screening of column, buffer, and solvent was often impractical due to the difficulty in tracking peaks from run to run—especially if MS detection was not used.

A new system for automated method development includes several key technologies that enable its application to the task of column characterization such as instrument control, MS peak matching, and project management.

In this tutorial, Advanced Chemistry Development’s AutoChrom is applied to the task of rigorous column characterization. AutoChrom has the ability to control the instrument, conduct MS-based peak tracking, and provides overall project management to analyze a large, diverse set of compounds, executing column, buffer, and mobile-phase screening to compare stationary-phase selectivity for several hundred analytes with varying solvents and buffers.

Using software to control the instrument and data system, AutoChrom automatically configures the system and creates complex injection sequences that reduce the amount of manual effort required by the user. Modern instrumentation such as column switchers and additional valves provide increased versatility in experiments.

Chemometric Peak Tracking

After the screening data is acquired, tracking peaks from run-to-run can be difficult, tedious, and time-consuming, particularly when dealing with complicated samples. AutoChrom will automatically match peaks between datasets, based on spectral similarity between components.

For LC/UV (DAD, PDA) and LC/MS data, the software extracts the pure component spectrum for each peak, and matches peaks accordingly. Reliable automated peak matching significantly reduces the amount of work required by the user, and provides fast data extraction. This makes it much more feasible to rigorously investigate chromatographic conditions, without overwhelming the analyst with data for manual review.

Project Management

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Figure 1. The AutoChrom Method development project viewer

When a large number of data files are collected, it can be a challenge to manage the data. The AutoChrom system links injections to the purpose for which they were performed, resulting in a clear workflow. The user is presented with a clear, concise overview of the project, and yet still retains access to the raw data, for future reference, validation, and auditing purposes (Figure 1).

Experimental

Several reversed-phase selectivity experiments were performed to probe the selectivity of the stationary phases using a set of 180 compounds, covering a broad range of pKa (0–10) and logP (-2–5) values. The compounds were split into five test mixtures to eliminate isobaric compounds in a single chromatographic run. The methods that were evaluated are summarized in the Table.

The data was collected using a Waters Alliance 2795 with MS detection. The flow rate was 1.0 mL/min, and the temperature was 35ºC. An electrospray ion source operating alternatively in positive ion and negative ion mode was used. Column selection was automated using a Rheodyne six-position column switcher. Data was acquired in full-scan mode under generic source conditions.

The data was processed and managed using AutoChrom. The resulting peak table was exported to Minitab software for statistical analysis.

Results and Discussion

Plotting the log of the capacity factors obtained on one stationary phase versus another (so called k-k plots) can measure the difference (or similarity) between the phases. A high degree of correlation means that similar chromatographic selectivity, and thus similar dominant retention mechanisms, exist between the phases.

Figure 2 presents the k-k plot generated comparing the Ascentis C18 and the Ascentis Express C18 phases. Strong correlation (R2=0.990) of the data demonstrates the expected similarity of selectivity between the two alkyl phases.

Figure 3 shows the k-k plot generated between the Ascentis C18 and the Ascentis Phenyl phases. The change in stationary phase chemistry results in some differences in retention and selectivity

(m = 0.81, R2= 0.68).

It is worth noting that many of the compounds, however, lie close to the line of identity, with a few showing large differences in selectivity. Examination of the data shows that the Ascentis Phenyl phase provides improved selectivity for selected basic, aromatic compounds.

Figure 4 shows a comparison of neutral and low pH mobile phases on the Ascentis C18 column. Visual examination shows more scatter, indicating that most compounds exhibited a change in selectivity with the change in pH.

Conclusion

With the enabling technologies of chemometric peak tracking, MS detection, and automated method development software, a new strategy was presented for the difficult task of method screening. Even with a large number of analytes and complex samples, it was possible to evaluate a greater amount of data with relatively little manual effort, making stationary-phase characterization more efficient and thorough than has been possible in the past.

Screening is an important part of method development, and the automated approach demonstrated here will make it easier for chromatographers to perform these experiments and process the data.

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